Staging the axilla in breast cancer patients with 18F-FDG PET: how small are the metastases that we can detect with ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-02-22

AUTHORS

Dimitri Bellevre, Cécile Blanc Fournier, Odile Switsers, Audrey Emmanuelle Dugué, Christelle Levy, Djelila Allouache, Cédric Desmonts, Hubert Crouet, Jean-Marc Guilloit, Jean-Michel Grellard, Nicolas Aide

ABSTRACT

PurposePoint spread function (PSF) reconstruction improves spatial resolution throughout the entire field of view of a PET system and can detect smaller metastatic deposits than conventional algorithms such as OSEM. We assessed the impact of PSF reconstruction on quantitative values and diagnostic accuracy for axillary staging of breast cancer patients, compared with an OSEM reconstruction, with emphasis on the size of nodal metastases.MethodsThis was a prospective study in a single referral centre in which 50 patients underwent an 18F-FDG PET examination before axillary lymph node dissection. PET data were reconstructed with an OSEM algorithm and PSF reconstruction, analysed blindly and validated by a pathologist who measured the largest nodal metastasis per axilla. This size was used to evaluate PET diagnostic performance.ResultsOn pathology, 34 patients (68 %) had nodal involvement. Overall, the median size of the largest nodal metastasis per axilla was 7 mm (range 0.5 – 40 mm). PSF reconstruction detected more involved nodes than OSEM reconstruction (p = 0.003). The mean PSF to OSEM SUVmax ratio was 1.66 (95 % CI 1.01 – 2.32). The sensitivities of PSF and OSEM reconstructions were, respectively, 96 % and 92 % in patients with a largest nodal metastasis of >7 mm, 60 % and 40 % in patients with a largest nodal metastasis of ≤7 mm, and 92 % and 69 % in patients with a primary tumour ≤30 mm. Biggerstaff graphical comparison showed that globally PSF reconstruction was superior to OSEM reconstruction. The median sizes of the largest nodal metastasis in patients with nodal involvement not detected by either PSF or OSEM reconstruction, detected by PSF but not by OSEM reconstruction and detected by both reconstructions were 3, 6 and 16 mm (p = 0.0064) respectively. In patients with nodal involvement detected by PSF reconstruction but not by OSEM reconstruction, the smallest detectable metastasis was 1.8 mm.ConclusionAs a result of better activity recovery, PET with PSF reconstruction performed better than PET with OSEM reconstruction in detecting nodal metastases ≤7 mm. However, its sensitivity is still insufficient for it to replace surgical approaches for axillary staging. PET with PSF reconstruction could be used to perform sentinel node biopsy more safely in patients with a primary tumour ≤30 mm and with unremarkable PET results in the axilla. More... »

PAGES

1103-1112

References to SciGraph publications

  • 2013-05-15. Optimisation and harmonisation: two sides of the same coin? in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2012-07-10. Diagnostic and prognostic correlates of preoperative FDG PET for breast cancer in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2011-08-18. SUVref: reducing reconstruction-dependent variation in PET SUV in EJNMMI RESEARCH
  • 2010-02-04. Feasibility of FDG PET/CT to monitor the response of axillary lymph node metastases to neoadjuvant chemotherapy in breast cancer patients in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2012-09-20. Pretreatment PET in breast cancer: is there a role? in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2012-09-14. Preoperative FDG PET/CT in breast cancer patients: where are we going? in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2009-05-16. Should FDG PET/CT be used for the initial staging of breast cancer? in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2009-05-05. Diagnostic value of full-dose FDG PET/CT for axillary lymph node staging in breast cancer patients in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2011-12-30. Prognostic Value of Number of Removed Lymph Nodes, Number of Involved Lymph Nodes, and Lymph Node Ratio in 7502 Breast Cancer Patients Enrolled onto Trials of the Austrian Breast and Colorectal Cancer Study Group (ABCSG) in ANNALS OF SURGICAL ONCOLOGY
  • 2009-11-14. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0 in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2013-04-06. Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2010-01-27. High throughput static and dynamic small animal imaging using clinical PET/CT: potential preclinical applications in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2013-06-11. Evaluation of strategies towards harmonization of FDG PET/CT studies in multicentre trials: comparison of scanner validation phantoms and data analysis procedures in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2009-07-25. A different role for FDG PET/CT in axillary lymph node staging in breast cancer in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2013-08-09. Sequential 18F-FDG PET/CT for early prediction of complete pathological response in breast and axilla during neoadjuvant chemotherapy in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00259-014-2689-7

    DOI

    http://dx.doi.org/10.1007/s00259-014-2689-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1024627703

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24562642


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1112", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Oncology and Carcinogenesis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Algorithms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Axilla", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Breast Neoplasms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Carcinoma, Ductal, Breast", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Female", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Fluorodeoxyglucose F18", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Image Processing, Computer-Assisted", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Limit of Detection", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Lymphatic Metastasis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Middle Aged", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Multimodal Imaging", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Positron-Emission Tomography", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Radiopharmaceuticals", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Tomography, X-Ray Computed", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Nuclear Medicine Department, Fran\u00e7ois Baclesse Cancer Centre, Avenue G\u00e9n\u00e9ral Harris, 14076, Cedex 5, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Nuclear Medicine Department, Fran\u00e7ois Baclesse Cancer Centre, Avenue G\u00e9n\u00e9ral Harris, 14076, Cedex 5, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bellevre", 
            "givenName": "Dimitri", 
            "id": "sg:person.01332540600.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01332540600.34"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Pathology Department, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Pathology Department, Fran\u00e7ois Baclesse Cancer Centre, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Blanc Fournier", 
            "givenName": "C\u00e9cile", 
            "id": "sg:person.01316272307.22", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01316272307.22"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Breast Cancer Unit, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Nuclear Medicine Department, Fran\u00e7ois Baclesse Cancer Centre, Avenue G\u00e9n\u00e9ral Harris, 14076, Cedex 5, Caen, France", 
                "Breast Cancer Unit, Fran\u00e7ois Baclesse Cancer Centre, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Switsers", 
            "givenName": "Odile", 
            "id": "sg:person.01055271505.47", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01055271505.47"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Biostatistics and Clinical Research unit, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Biostatistics and Clinical Research unit, Fran\u00e7ois Baclesse Cancer Centre, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Dugu\u00e9", 
            "givenName": "Audrey Emmanuelle", 
            "id": "sg:person.0744145733.83", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0744145733.83"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Radiation Oncology Department, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Breast Cancer Unit, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
                "Radiation Oncology Department, Fran\u00e7ois Baclesse Cancer Centre, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Levy", 
            "givenName": "Christelle", 
            "id": "sg:person.01117111471.08", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01117111471.08"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Breast Cancer Unit, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Breast Cancer Unit, Fran\u00e7ois Baclesse Cancer Centre, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Allouache", 
            "givenName": "Djelila", 
            "id": "sg:person.01321533125.93", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01321533125.93"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Medical Physics Department, University Hospital, Caen, France", 
              "id": "http://www.grid.ac/institutes/grid.411149.8", 
              "name": [
                "Medical Physics Department, University Hospital, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Desmonts", 
            "givenName": "C\u00e9dric", 
            "id": "sg:person.0715336115.22", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0715336115.22"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Surgical Oncology, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Surgical Oncology, Fran\u00e7ois Baclesse Cancer Centre, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Crouet", 
            "givenName": "Hubert", 
            "id": "sg:person.01167733127.48", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01167733127.48"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Surgical Oncology, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Surgical Oncology, Fran\u00e7ois Baclesse Cancer Centre, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Guilloit", 
            "givenName": "Jean-Marc", 
            "id": "sg:person.01312640760.23", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312640760.23"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Biostatistics and Clinical Research unit, Fran\u00e7ois Baclesse Cancer Centre, Caen, France", 
              "id": "http://www.grid.ac/institutes/None", 
              "name": [
                "Biostatistics and Clinical Research unit, Fran\u00e7ois Baclesse Cancer Centre, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Grellard", 
            "givenName": "Jean-Michel", 
            "id": "sg:person.01270266500.90", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01270266500.90"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Normandie Universit\u00e9, Caen, France", 
              "id": "http://www.grid.ac/institutes/grid.460771.3", 
              "name": [
                "Nuclear Medicine Department, Fran\u00e7ois Baclesse Cancer Centre, Avenue G\u00e9n\u00e9ral Harris, 14076, Cedex 5, Caen, France", 
                "Normandie Universit\u00e9, Caen, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Aide", 
            "givenName": "Nicolas", 
            "id": "sg:person.01152406451.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152406451.51"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00259-012-2245-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028562458", 
              "https://doi.org/10.1007/s00259-012-2245-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-009-1352-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053655973", 
              "https://doi.org/10.1007/s00259-009-1352-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-009-1297-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021250910", 
              "https://doi.org/10.1007/s00259-009-1297-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/2191-219x-1-16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025107920", 
              "https://doi.org/10.1186/2191-219x-1-16"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1245/s10434-011-2189-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006528530", 
              "https://doi.org/10.1245/s10434-011-2189-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-013-2391-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016046039", 
              "https://doi.org/10.1007/s00259-013-2391-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-009-1145-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042070845", 
              "https://doi.org/10.1007/s00259-009-1145-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-012-2216-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026357947", 
              "https://doi.org/10.1007/s00259-012-2216-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-013-2465-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016939976", 
              "https://doi.org/10.1007/s00259-013-2465-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-009-1211-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014803456", 
              "https://doi.org/10.1007/s00259-009-1211-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-009-1343-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015152995", 
              "https://doi.org/10.1007/s00259-009-1343-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-013-2515-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038009196", 
              "https://doi.org/10.1007/s00259-013-2515-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-009-1159-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011927699", 
              "https://doi.org/10.1007/s00259-009-1159-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-012-2181-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025496974", 
              "https://doi.org/10.1007/s00259-012-2181-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00259-013-2440-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044598631", 
              "https://doi.org/10.1007/s00259-013-2440-9"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2014-02-22", 
        "datePublishedReg": "2014-02-22", 
        "description": "PurposePoint spread function (PSF) reconstruction improves spatial resolution throughout the entire field of view of a PET system and can detect smaller metastatic deposits than conventional algorithms such as OSEM. We assessed the impact of PSF reconstruction on quantitative values and diagnostic accuracy for axillary staging of breast cancer patients, compared with an OSEM reconstruction, with emphasis on the size of nodal metastases.MethodsThis was a prospective study in a single referral centre in which 50 patients underwent an 18F-FDG PET examination before axillary lymph node dissection. PET data were reconstructed with an OSEM algorithm and PSF reconstruction, analysed blindly and validated by a pathologist who measured the largest nodal metastasis per axilla. This size was used to evaluate PET diagnostic performance.ResultsOn pathology, 34 patients (68\u00a0%) had nodal involvement. Overall, the median size of the largest nodal metastasis per axilla was 7\u00a0mm (range 0.5\u00a0\u2013\u00a040\u00a0mm). PSF reconstruction detected more involved nodes than OSEM reconstruction (p\u2009=\u20090.003). The mean PSF to OSEM SUVmax ratio was 1.66 (95\u00a0% CI 1.01\u00a0\u2013\u00a02.32). The sensitivities of PSF and OSEM reconstructions were, respectively, 96\u00a0% and 92\u00a0% in patients with a largest nodal metastasis of >7\u00a0mm, 60\u00a0% and 40\u00a0% in patients with a largest nodal metastasis of \u22647\u00a0mm, and 92\u00a0% and 69\u00a0% in patients with a primary tumour \u226430\u00a0mm. Biggerstaff graphical comparison showed that globally PSF reconstruction was superior to OSEM reconstruction. The median sizes of the largest nodal metastasis in patients with nodal involvement not detected by either PSF or OSEM reconstruction, detected by PSF but not by OSEM reconstruction and detected by both reconstructions were 3, 6 and 16\u00a0mm (p\u2009=\u20090.0064) respectively. In patients with nodal involvement detected by PSF reconstruction but not by OSEM reconstruction, the smallest detectable metastasis was 1.8\u00a0mm.ConclusionAs a result of better activity recovery, PET with PSF reconstruction performed better than PET with OSEM reconstruction in detecting nodal metastases \u22647\u00a0mm. However, its sensitivity is still insufficient for it to replace surgical approaches for axillary staging. PET with PSF reconstruction could be used to perform sentinel node biopsy more safely in patients with a primary tumour \u226430\u00a0mm and with unremarkable PET results in the axilla.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s00259-014-2689-7", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1297401", 
            "issn": [
              "1619-7070", 
              "1619-7089"
            ], 
            "name": "European Journal of Nuclear Medicine and Molecular Imaging", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "6", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "41"
          }
        ], 
        "keywords": [
          "largest nodal metastasis", 
          "nodal metastasis", 
          "breast cancer patients", 
          "nodal involvement", 
          "axillary staging", 
          "cancer patients", 
          "primary tumor", 
          "axillary lymph node dissection", 
          "single referral center", 
          "lymph node dissection", 
          "sentinel node biopsy", 
          "small metastatic deposits", 
          "node dissection", 
          "median size", 
          "SUVmax ratio", 
          "referral center", 
          "node biopsy", 
          "metastatic deposits", 
          "prospective study", 
          "detectable metastases", 
          "surgical approach", 
          "PET results", 
          "PET examinations", 
          "patients", 
          "metastasis", 
          "axilla", 
          "involved nodes", 
          "diagnostic accuracy", 
          "diagnostic performance", 
          "tumors", 
          "staging", 
          "involvement", 
          "PET", 
          "PSF reconstruction", 
          "PET data", 
          "MethodsThis", 
          "biopsy", 
          "OSEM reconstruction", 
          "pathology", 
          "dissection", 
          "pathologists", 
          "PurposePoint", 
          "ConclusionAs", 
          "better activity recovery", 
          "examination", 
          "reconstruction", 
          "sensitivity", 
          "recovery", 
          "center", 
          "study", 
          "quantitative values", 
          "results", 
          "OSEM algorithm", 
          "size", 
          "data", 
          "clinical PET systems", 
          "ratio", 
          "impact", 
          "nodes", 
          "comparison", 
          "OSEM", 
          "function reconstruction", 
          "PET system", 
          "PSF", 
          "emphasis", 
          "values", 
          "system", 
          "approach", 
          "entire field", 
          "view", 
          "resolution", 
          "deposits", 
          "activity recovery", 
          "accuracy", 
          "spatial resolution", 
          "performance", 
          "graphical comparison", 
          "field", 
          "algorithm", 
          "conventional algorithms"
        ], 
        "name": "Staging the axilla in breast cancer patients with 18F-FDG PET: how small are the metastases that we can detect with new generation clinical PET systems?", 
        "pagination": "1103-1112", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1024627703"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00259-014-2689-7"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "24562642"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00259-014-2689-7", 
          "https://app.dimensions.ai/details/publication/pub.1024627703"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:40", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_636.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s00259-014-2689-7"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00259-014-2689-7'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00259-014-2689-7'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00259-014-2689-7'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00259-014-2689-7'


     

    This table displays all metadata directly associated to this object as RDF triples.

    348 TRIPLES      21 PREDICATES      135 URIs      112 LITERALS      22 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00259-014-2689-7 schema:about N0eedbe2694a2437cae68aa51bfb52232
    2 N0f4268464b3f4141b46e453b6b355f20
    3 N133fbb27b1a543b0b9543ee469608686
    4 N17a5c43a7b3a4c16aa5e8e98efa4b0a3
    5 N4513a031a6044adb91c1bee90c85d027
    6 N4f24e346367b4cc4a361524c0e1b6697
    7 N515c18ab1d804f6caa1e5d4ba689d353
    8 N56f4b5d88538459b8c0d1a08b7f840d0
    9 N5f60f86d4a5449d7a8c87ba69d3b8fec
    10 N67aaab94a008439cbffc198efaf220cb
    11 N6ce721524431492a8461fdc49e39bfbd
    12 Nb266af3f88bd46608b930fa0892dc071
    13 Nb30452be8b034d538184cff97c774475
    14 Nebdaabaf022240848d76165330374ab8
    15 Nff0d56a59bc14b48919b532f977a45d7
    16 anzsrc-for:11
    17 anzsrc-for:1112
    18 schema:author N2f3ab34ab7fd45618ba7e212ee2e9b2e
    19 schema:citation sg:pub.10.1007/s00259-009-1145-6
    20 sg:pub.10.1007/s00259-009-1159-0
    21 sg:pub.10.1007/s00259-009-1211-0
    22 sg:pub.10.1007/s00259-009-1297-4
    23 sg:pub.10.1007/s00259-009-1343-2
    24 sg:pub.10.1007/s00259-009-1352-1
    25 sg:pub.10.1007/s00259-012-2181-1
    26 sg:pub.10.1007/s00259-012-2216-7
    27 sg:pub.10.1007/s00259-012-2245-2
    28 sg:pub.10.1007/s00259-013-2391-1
    29 sg:pub.10.1007/s00259-013-2440-9
    30 sg:pub.10.1007/s00259-013-2465-0
    31 sg:pub.10.1007/s00259-013-2515-7
    32 sg:pub.10.1186/2191-219x-1-16
    33 sg:pub.10.1245/s10434-011-2189-y
    34 schema:datePublished 2014-02-22
    35 schema:datePublishedReg 2014-02-22
    36 schema:description PurposePoint spread function (PSF) reconstruction improves spatial resolution throughout the entire field of view of a PET system and can detect smaller metastatic deposits than conventional algorithms such as OSEM. We assessed the impact of PSF reconstruction on quantitative values and diagnostic accuracy for axillary staging of breast cancer patients, compared with an OSEM reconstruction, with emphasis on the size of nodal metastases.MethodsThis was a prospective study in a single referral centre in which 50 patients underwent an 18F-FDG PET examination before axillary lymph node dissection. PET data were reconstructed with an OSEM algorithm and PSF reconstruction, analysed blindly and validated by a pathologist who measured the largest nodal metastasis per axilla. This size was used to evaluate PET diagnostic performance.ResultsOn pathology, 34 patients (68 %) had nodal involvement. Overall, the median size of the largest nodal metastasis per axilla was 7 mm (range 0.5 – 40 mm). PSF reconstruction detected more involved nodes than OSEM reconstruction (p = 0.003). The mean PSF to OSEM SUVmax ratio was 1.66 (95 % CI 1.01 – 2.32). The sensitivities of PSF and OSEM reconstructions were, respectively, 96 % and 92 % in patients with a largest nodal metastasis of >7 mm, 60 % and 40 % in patients with a largest nodal metastasis of ≤7 mm, and 92 % and 69 % in patients with a primary tumour ≤30 mm. Biggerstaff graphical comparison showed that globally PSF reconstruction was superior to OSEM reconstruction. The median sizes of the largest nodal metastasis in patients with nodal involvement not detected by either PSF or OSEM reconstruction, detected by PSF but not by OSEM reconstruction and detected by both reconstructions were 3, 6 and 16 mm (p = 0.0064) respectively. In patients with nodal involvement detected by PSF reconstruction but not by OSEM reconstruction, the smallest detectable metastasis was 1.8 mm.ConclusionAs a result of better activity recovery, PET with PSF reconstruction performed better than PET with OSEM reconstruction in detecting nodal metastases ≤7 mm. However, its sensitivity is still insufficient for it to replace surgical approaches for axillary staging. PET with PSF reconstruction could be used to perform sentinel node biopsy more safely in patients with a primary tumour ≤30 mm and with unremarkable PET results in the axilla.
    37 schema:genre article
    38 schema:isAccessibleForFree true
    39 schema:isPartOf N840d876dae10454b9928127791fb6ac7
    40 Nf0c5c2568e5b4ac5938a56e8571f5199
    41 sg:journal.1297401
    42 schema:keywords ConclusionAs
    43 MethodsThis
    44 OSEM
    45 OSEM algorithm
    46 OSEM reconstruction
    47 PET
    48 PET data
    49 PET examinations
    50 PET results
    51 PET system
    52 PSF
    53 PSF reconstruction
    54 PurposePoint
    55 SUVmax ratio
    56 accuracy
    57 activity recovery
    58 algorithm
    59 approach
    60 axilla
    61 axillary lymph node dissection
    62 axillary staging
    63 better activity recovery
    64 biopsy
    65 breast cancer patients
    66 cancer patients
    67 center
    68 clinical PET systems
    69 comparison
    70 conventional algorithms
    71 data
    72 deposits
    73 detectable metastases
    74 diagnostic accuracy
    75 diagnostic performance
    76 dissection
    77 emphasis
    78 entire field
    79 examination
    80 field
    81 function reconstruction
    82 graphical comparison
    83 impact
    84 involved nodes
    85 involvement
    86 largest nodal metastasis
    87 lymph node dissection
    88 median size
    89 metastasis
    90 metastatic deposits
    91 nodal involvement
    92 nodal metastasis
    93 node biopsy
    94 node dissection
    95 nodes
    96 pathologists
    97 pathology
    98 patients
    99 performance
    100 primary tumor
    101 prospective study
    102 quantitative values
    103 ratio
    104 reconstruction
    105 recovery
    106 referral center
    107 resolution
    108 results
    109 sensitivity
    110 sentinel node biopsy
    111 single referral center
    112 size
    113 small metastatic deposits
    114 spatial resolution
    115 staging
    116 study
    117 surgical approach
    118 system
    119 tumors
    120 values
    121 view
    122 schema:name Staging the axilla in breast cancer patients with 18F-FDG PET: how small are the metastases that we can detect with new generation clinical PET systems?
    123 schema:pagination 1103-1112
    124 schema:productId N8b2560f0553547d794452691942ea811
    125 N9514d75383c54d998f9006f6f08419e1
    126 Ndf0eb1fbd4e14804aa10ada1f58f9541
    127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024627703
    128 https://doi.org/10.1007/s00259-014-2689-7
    129 schema:sdDatePublished 2022-10-01T06:40
    130 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    131 schema:sdPublisher N59ad471ed6bf45afba66dad9b8d9c7ef
    132 schema:url https://doi.org/10.1007/s00259-014-2689-7
    133 sgo:license sg:explorer/license/
    134 sgo:sdDataset articles
    135 rdf:type schema:ScholarlyArticle
    136 N02ef941298404d5c8a24f5229fdb487f rdf:first sg:person.0715336115.22
    137 rdf:rest Nb5ec2699401f45c2889fd6ff16e2cca3
    138 N0eedbe2694a2437cae68aa51bfb52232 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    139 schema:name Multimodal Imaging
    140 rdf:type schema:DefinedTerm
    141 N0f4268464b3f4141b46e453b6b355f20 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    142 schema:name Lymphatic Metastasis
    143 rdf:type schema:DefinedTerm
    144 N10181aa5dd7343dd8470f5682b385eb2 rdf:first sg:person.01312640760.23
    145 rdf:rest Nc8d2936e3a8a405dbabe92d236a3a60e
    146 N133fbb27b1a543b0b9543ee469608686 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    147 schema:name Radiopharmaceuticals
    148 rdf:type schema:DefinedTerm
    149 N17a5c43a7b3a4c16aa5e8e98efa4b0a3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    150 schema:name Female
    151 rdf:type schema:DefinedTerm
    152 N1a6bce16ea6a40288164bd1835ab3d5e rdf:first sg:person.01316272307.22
    153 rdf:rest N65079e17cf884b968a7a9b4f908a083c
    154 N21374f9feb8b4a27bd92f6de1fabb398 rdf:first sg:person.01321533125.93
    155 rdf:rest N02ef941298404d5c8a24f5229fdb487f
    156 N2f3ab34ab7fd45618ba7e212ee2e9b2e rdf:first sg:person.01332540600.34
    157 rdf:rest N1a6bce16ea6a40288164bd1835ab3d5e
    158 N4513a031a6044adb91c1bee90c85d027 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    159 schema:name Axilla
    160 rdf:type schema:DefinedTerm
    161 N4f24e346367b4cc4a361524c0e1b6697 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    162 schema:name Limit of Detection
    163 rdf:type schema:DefinedTerm
    164 N515c18ab1d804f6caa1e5d4ba689d353 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    165 schema:name Breast Neoplasms
    166 rdf:type schema:DefinedTerm
    167 N56f4b5d88538459b8c0d1a08b7f840d0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    168 schema:name Image Processing, Computer-Assisted
    169 rdf:type schema:DefinedTerm
    170 N59ad471ed6bf45afba66dad9b8d9c7ef schema:name Springer Nature - SN SciGraph project
    171 rdf:type schema:Organization
    172 N5f60f86d4a5449d7a8c87ba69d3b8fec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    173 schema:name Positron-Emission Tomography
    174 rdf:type schema:DefinedTerm
    175 N65079e17cf884b968a7a9b4f908a083c rdf:first sg:person.01055271505.47
    176 rdf:rest N66fee7e045254303a311ff1b6012da6e
    177 N66fee7e045254303a311ff1b6012da6e rdf:first sg:person.0744145733.83
    178 rdf:rest N8422e677d79548b490d0b4d848e57127
    179 N67aaab94a008439cbffc198efaf220cb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    180 schema:name Tomography, X-Ray Computed
    181 rdf:type schema:DefinedTerm
    182 N6ce721524431492a8461fdc49e39bfbd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    183 schema:name Humans
    184 rdf:type schema:DefinedTerm
    185 N840d876dae10454b9928127791fb6ac7 schema:volumeNumber 41
    186 rdf:type schema:PublicationVolume
    187 N8422e677d79548b490d0b4d848e57127 rdf:first sg:person.01117111471.08
    188 rdf:rest N21374f9feb8b4a27bd92f6de1fabb398
    189 N8b2560f0553547d794452691942ea811 schema:name pubmed_id
    190 schema:value 24562642
    191 rdf:type schema:PropertyValue
    192 N9514d75383c54d998f9006f6f08419e1 schema:name dimensions_id
    193 schema:value pub.1024627703
    194 rdf:type schema:PropertyValue
    195 Nb266af3f88bd46608b930fa0892dc071 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    196 schema:name Algorithms
    197 rdf:type schema:DefinedTerm
    198 Nb30452be8b034d538184cff97c774475 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    199 schema:name Carcinoma, Ductal, Breast
    200 rdf:type schema:DefinedTerm
    201 Nb5ec2699401f45c2889fd6ff16e2cca3 rdf:first sg:person.01167733127.48
    202 rdf:rest N10181aa5dd7343dd8470f5682b385eb2
    203 Nb6e4a5633a13485781a9840c720207c6 rdf:first sg:person.01152406451.51
    204 rdf:rest rdf:nil
    205 Nc8d2936e3a8a405dbabe92d236a3a60e rdf:first sg:person.01270266500.90
    206 rdf:rest Nb6e4a5633a13485781a9840c720207c6
    207 Ndf0eb1fbd4e14804aa10ada1f58f9541 schema:name doi
    208 schema:value 10.1007/s00259-014-2689-7
    209 rdf:type schema:PropertyValue
    210 Nebdaabaf022240848d76165330374ab8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    211 schema:name Fluorodeoxyglucose F18
    212 rdf:type schema:DefinedTerm
    213 Nf0c5c2568e5b4ac5938a56e8571f5199 schema:issueNumber 6
    214 rdf:type schema:PublicationIssue
    215 Nff0d56a59bc14b48919b532f977a45d7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    216 schema:name Middle Aged
    217 rdf:type schema:DefinedTerm
    218 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    219 schema:name Medical and Health Sciences
    220 rdf:type schema:DefinedTerm
    221 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
    222 schema:name Oncology and Carcinogenesis
    223 rdf:type schema:DefinedTerm
    224 sg:journal.1297401 schema:issn 1619-7070
    225 1619-7089
    226 schema:name European Journal of Nuclear Medicine and Molecular Imaging
    227 schema:publisher Springer Nature
    228 rdf:type schema:Periodical
    229 sg:person.01055271505.47 schema:affiliation grid-institutes:None
    230 schema:familyName Switsers
    231 schema:givenName Odile
    232 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01055271505.47
    233 rdf:type schema:Person
    234 sg:person.01117111471.08 schema:affiliation grid-institutes:None
    235 schema:familyName Levy
    236 schema:givenName Christelle
    237 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01117111471.08
    238 rdf:type schema:Person
    239 sg:person.01152406451.51 schema:affiliation grid-institutes:grid.460771.3
    240 schema:familyName Aide
    241 schema:givenName Nicolas
    242 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152406451.51
    243 rdf:type schema:Person
    244 sg:person.01167733127.48 schema:affiliation grid-institutes:None
    245 schema:familyName Crouet
    246 schema:givenName Hubert
    247 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01167733127.48
    248 rdf:type schema:Person
    249 sg:person.01270266500.90 schema:affiliation grid-institutes:None
    250 schema:familyName Grellard
    251 schema:givenName Jean-Michel
    252 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01270266500.90
    253 rdf:type schema:Person
    254 sg:person.01312640760.23 schema:affiliation grid-institutes:None
    255 schema:familyName Guilloit
    256 schema:givenName Jean-Marc
    257 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01312640760.23
    258 rdf:type schema:Person
    259 sg:person.01316272307.22 schema:affiliation grid-institutes:None
    260 schema:familyName Blanc Fournier
    261 schema:givenName Cécile
    262 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01316272307.22
    263 rdf:type schema:Person
    264 sg:person.01321533125.93 schema:affiliation grid-institutes:None
    265 schema:familyName Allouache
    266 schema:givenName Djelila
    267 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01321533125.93
    268 rdf:type schema:Person
    269 sg:person.01332540600.34 schema:affiliation grid-institutes:None
    270 schema:familyName Bellevre
    271 schema:givenName Dimitri
    272 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01332540600.34
    273 rdf:type schema:Person
    274 sg:person.0715336115.22 schema:affiliation grid-institutes:grid.411149.8
    275 schema:familyName Desmonts
    276 schema:givenName Cédric
    277 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0715336115.22
    278 rdf:type schema:Person
    279 sg:person.0744145733.83 schema:affiliation grid-institutes:None
    280 schema:familyName Dugué
    281 schema:givenName Audrey Emmanuelle
    282 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0744145733.83
    283 rdf:type schema:Person
    284 sg:pub.10.1007/s00259-009-1145-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042070845
    285 https://doi.org/10.1007/s00259-009-1145-6
    286 rdf:type schema:CreativeWork
    287 sg:pub.10.1007/s00259-009-1159-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011927699
    288 https://doi.org/10.1007/s00259-009-1159-0
    289 rdf:type schema:CreativeWork
    290 sg:pub.10.1007/s00259-009-1211-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014803456
    291 https://doi.org/10.1007/s00259-009-1211-0
    292 rdf:type schema:CreativeWork
    293 sg:pub.10.1007/s00259-009-1297-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021250910
    294 https://doi.org/10.1007/s00259-009-1297-4
    295 rdf:type schema:CreativeWork
    296 sg:pub.10.1007/s00259-009-1343-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015152995
    297 https://doi.org/10.1007/s00259-009-1343-2
    298 rdf:type schema:CreativeWork
    299 sg:pub.10.1007/s00259-009-1352-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053655973
    300 https://doi.org/10.1007/s00259-009-1352-1
    301 rdf:type schema:CreativeWork
    302 sg:pub.10.1007/s00259-012-2181-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025496974
    303 https://doi.org/10.1007/s00259-012-2181-1
    304 rdf:type schema:CreativeWork
    305 sg:pub.10.1007/s00259-012-2216-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026357947
    306 https://doi.org/10.1007/s00259-012-2216-7
    307 rdf:type schema:CreativeWork
    308 sg:pub.10.1007/s00259-012-2245-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028562458
    309 https://doi.org/10.1007/s00259-012-2245-2
    310 rdf:type schema:CreativeWork
    311 sg:pub.10.1007/s00259-013-2391-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016046039
    312 https://doi.org/10.1007/s00259-013-2391-1
    313 rdf:type schema:CreativeWork
    314 sg:pub.10.1007/s00259-013-2440-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044598631
    315 https://doi.org/10.1007/s00259-013-2440-9
    316 rdf:type schema:CreativeWork
    317 sg:pub.10.1007/s00259-013-2465-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016939976
    318 https://doi.org/10.1007/s00259-013-2465-0
    319 rdf:type schema:CreativeWork
    320 sg:pub.10.1007/s00259-013-2515-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038009196
    321 https://doi.org/10.1007/s00259-013-2515-7
    322 rdf:type schema:CreativeWork
    323 sg:pub.10.1186/2191-219x-1-16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025107920
    324 https://doi.org/10.1186/2191-219x-1-16
    325 rdf:type schema:CreativeWork
    326 sg:pub.10.1245/s10434-011-2189-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1006528530
    327 https://doi.org/10.1245/s10434-011-2189-y
    328 rdf:type schema:CreativeWork
    329 grid-institutes:None schema:alternateName Biostatistics and Clinical Research unit, François Baclesse Cancer Centre, Caen, France
    330 Breast Cancer Unit, François Baclesse Cancer Centre, Caen, France
    331 Nuclear Medicine Department, François Baclesse Cancer Centre, Avenue Général Harris, 14076, Cedex 5, Caen, France
    332 Pathology Department, François Baclesse Cancer Centre, Caen, France
    333 Radiation Oncology Department, François Baclesse Cancer Centre, Caen, France
    334 Surgical Oncology, François Baclesse Cancer Centre, Caen, France
    335 schema:name Biostatistics and Clinical Research unit, François Baclesse Cancer Centre, Caen, France
    336 Breast Cancer Unit, François Baclesse Cancer Centre, Caen, France
    337 Nuclear Medicine Department, François Baclesse Cancer Centre, Avenue Général Harris, 14076, Cedex 5, Caen, France
    338 Pathology Department, François Baclesse Cancer Centre, Caen, France
    339 Radiation Oncology Department, François Baclesse Cancer Centre, Caen, France
    340 Surgical Oncology, François Baclesse Cancer Centre, Caen, France
    341 rdf:type schema:Organization
    342 grid-institutes:grid.411149.8 schema:alternateName Medical Physics Department, University Hospital, Caen, France
    343 schema:name Medical Physics Department, University Hospital, Caen, France
    344 rdf:type schema:Organization
    345 grid-institutes:grid.460771.3 schema:alternateName Normandie Université, Caen, France
    346 schema:name Normandie Université, Caen, France
    347 Nuclear Medicine Department, François Baclesse Cancer Centre, Avenue Général Harris, 14076, Cedex 5, Caen, France
    348 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...